An improved MSCNN and GRU model for rolling bearing fault diagnosis

Wang, Teng (avtor), Tang, Youfu (avtor), Wang, Tao (avtor), Lei, Na (avtor)

In this paper, a novel fault diagnosis method based on the fusion of squeeze and excitation-multiscale convolutional neural networks (SENet-MSCNN) and gate recurrent unit (GRU) is proposed to address the problem of low diagnosis rate caused by the fact that normal samples are much larger than fault samples in the vibration big data. The method takes the time-domain vibration signal as input and fuses the spatial features extracted by SENet-MSCNN. The temporal features extracted by GRU in order to bring them into the fully connected layer for identification so as to realize the intelligent diagnosis of rolling bearing adaptive feature extraction. Finally, the method is applied to the simulated signal and experimental data for testing and analysis. The results reveal that the model can reach 98.98 % and 76.44 % migration diagnostic accuracy in bearing and gearbox datasets. At the same time, it has strong noise immunity, adaptivity, and robustness, providing an effective way for intelligent diagnosis of rolling bearing vibration big data.

• Jezik: Angleški jezik
• Ključne besede: SENet, multiscale convolutional neural networks, gate recurrent unit, rolling bearings, fault diagnosis
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FS - Fakulteta za strojništvo
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2023
• Št. strani: Str. 261-274
• Številčenje: Vol. 69, no. 5/6
• PID: 20.500.12556/RUL-146703
• UDK: 621.8
• ISSN pri članku: 0039-2480
• DOI: 10.5545/sv-jme.2022.459
• COBISS.SI-ID: 154984707
• Datum objave v RUL: 08.06.2023

Gradivo je del revije

• Naslov: Strojniški vestnik
• Skrajšan naslov: Stroj. vestn.
• Založnik: Zveza strojnih inženirjev in tehnikov Slovenije [etc.], = Association of Mechanical Engineers and Technicians of Slovenia [etc.
• ISSN: 0039-2480
• COBISS.SI-ID: 762116

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Naslov: Diagnosticiranje napak kotalnih ležajev na podlagi izboljšanih modelov MSCNN in NRU
• Ključne besede: SENet, MSCNN, GRU, kotalni ležaji, diagnosticiranje napak

Projekti

• Financer: Drugi - Drug financer ali več financerjev
• Program financ.: Youth Science Foundation of Northeast Petroleum University
• Številka projekta: 2018QNL-28